Abstract
The value of GPS data has generated a group of location-based services. Pick-up points recommendation by mining taxis’ trajectories can effectively both improve drivers’ profits and reduce oil consumption. However, existing methods always ignore the spatial-temporal features and the drivers’ preferences. Therefore, we propose to recommend a personalized sequence of pick-up points taking the two preceding factors into account. Firstly, we extract historical pick-up points from taxis’ trajectories and use these points to generate candidate ones by a novel approach of spatial-temporal analysis. Secondly, we devise a collaborative filtering algorithm to choose candidate points again. According to the location and the time of historical pick-up points, our system can give taxi-drivers an optimal sequence of pick-up points. Experimental results show that our method can obviously improve both the accuracy and the preference of candidate pick-up points for taxi-drivers.
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Acknowledgments
This work is supported by National Nature Science Foundation of China (61572187, 61370227, 61572186), Hunan Provincial Natural Science Foundation of China (2015JJ2056), Hunan Provincial University Innovation Platform Open Fund Project of China (14K037), General project of Hunan Provincial Education Department (16C0642).
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Liu, Y., Liu, J., Wang, J., Liao, Z., Tang, M. (2016). Recommending a Personalized Sequence of Pick-Up Points. In: Wang, G., Han, Y., Martínez Pérez, G. (eds) Advances in Services Computing. APSCC 2016. Lecture Notes in Computer Science(), vol 10065. Springer, Cham. https://doi.org/10.1007/978-3-319-49178-3_22
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DOI: https://doi.org/10.1007/978-3-319-49178-3_22
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